Causality-inspired Latent Feature Augmentation for Single Domain Generalization

TL;DR

This paper introduces a causality-inspired latent feature augmentation technique for single domain generalization, enhancing model robustness by generating diverse, stable causal features in latent space to improve performance on unseen domains.

Contribution

It proposes a novel causal learning-based feature augmentation method that generates implicit transformations in latent space, reducing reliance on finite image-level transformations for better generalization.

Findings

Outperforms state-of-the-art single domain generalization methods

Demonstrates robustness across multiple benchmarks

Effectively captures stable causal features

Abstract

Single domain generalization (Single-DG) intends to develop a generalizable model with only one single training domain to perform well on other unknown target domains. Under the domain-hungry configuration, how to expand the coverage of source domain and find intrinsic causal features across different distributions is the key to enhancing the models' generalization ability. Existing methods mainly depend on the meticulous design of finite image-level transformation techniques and learning invariant features across domains based on statistical correlation between samples and labels in source domain. This makes it difficult to capture stable semantics between source and target domains, which hinders the improvement of the model's generalization performance. In this paper, we propose a novel causality-inspired latent feature augmentation method for Single-DG by learning the meta-knowledge of feature-level transformation based on causal learning and interventions. Instead of strongly relying on the finite image-level transformation, with the learned meta-knowledge, we can generate diverse implicit feature-level transformations in latent space based on the consistency of causal features and diversity of non-causal features, which can better compensate for the domain-hungry defect and reduce the strong reliance on initial finite image-level transformations and capture more stable domain-invariant causal features for generalization. Extensive experiments on several open-access benchmarks demonstrate the outstanding performance of our model over other state-of-the-art single domain generalization and also multi-source domain generalization methods.